On the Iterative Decoding of High-Rate LDPC Codes With Applications in Compressed Sensing

TL;DR

This paper analyzes the performance of high-rate LDPC codes with message-passing decoding on erasure and symmetric channels, deriving scaling laws and extending the analysis to compressed sensing of sparse signals.

Contribution

It derives high-rate scaling laws for LDPC decoding thresholds and extends verification decoding analysis to compressed sensing, providing guarantees for sparse signal reconstruction.

Findings

Decoding thresholds scale as Θ(k^{-1}) for high-rate LDPC codes.

Critical stopping ratios scale as Θ(k^{-j/(j-2)}).

Sparse signals can be reconstructed efficiently with linear measurements.

Abstract

This paper considers the performance of $(j,k)$-regular low-density parity-check (LDPC) codes with message-passing (MP) decoding algorithms in the high-rate regime. In particular, we derive the high-rate scaling law for MP decoding of LDPC codes on the binary erasure channel (BEC) and the $q$-ary symmetric channel ($q$-SC). For the BEC, the density evolution (DE) threshold of iterative decoding scales like $\Theta(k^{-1})$ and the critical stopping ratio scales like $\Theta(k^{-j/(j-2)})$. For the $q$-SC, the DE threshold of verification decoding depends on the details of the decoder and scales like $\Theta(k^{-1})$ for one decoder. Using the fact that coding over large finite alphabets is very similar to coding over the real numbers, the analysis of verification decoding is also extended to the the compressed sensing (CS) of strictly-sparse signals. A DE based approach is used to analyze the CS systems with randomized-reconstruction guarantees. This leads to the result that strictly-sparse signals can be reconstructed efficiently with high-probability using a constant oversampling ratio (i.e., when the number of measurements scales linearly with the sparsity of the signal). A stopping-set based approach is also used to get stronger (e.g., uniform-in-probability) reconstruction guarantees.